Healthcare is one of the most exciting spaces for AI and generative AI innovation. There are myriad opportunities for leveraging these technologies to support clinicians, drive efficiencies, enhance the patient experience and improve health outcomes.
Providing timely patient insights to clinicians
Some healthcare providers are already using generative AI powered tools to summarise patient health records for clinicians, so they have relevant information and insights to hand about a patient when they arrive for their appointment.
Clinical decision support
According to research by IDC in August 2024, a quarter (26.1%) of healthcare respondents have a proof of concept for Generative AI in progress in the realm of clinical decision support systems.
AI continues to require a “human in the loop”, i.e. a clinician to provide oversight over the information provided. However, it has an important role to play in supporting clinicians in care delivery, whether surfacing information from disparate sources, offering insights to enable more personalised care, or in diagnostics.
Diagnostics
A key area where AI has been proven to enhance care is in medical diagnostics. Trials have shown successful applications in the interpretation of Xray and scan results in breast cancer diagnosis, for example. In the fields of radiology, pathology and dermatology, AI diagnostic capabilities can meet and even exceed those of clinicians. This makes diagnostics a particularly important area for AI deployment – helping to speed up diagnostic processes for better health outcomes.
AI-powered scheduling
AI is being used to predict demand and to optimise staffing schedules and the scheduling of appointments and other resources. This way, AI can help to reduce issues of bed blocking, under utilisation of resources and other scheduling issues – making day-to-day operation much more efficient.
Faster drug discovery
Work is being to undertaken to understand the potential of generative AI to assist with drug discovery and the potential to better combine drug treatments to deliver more effective results. In addition, AI is being used to more rapidly review and interpret the vast quantities of data generated as part of a drug development, testing and trial processes. It is hoped that, ultimately, AI will help to bring new drug treatments to market faster.
